scholarly journals Healthcare Prediction and Analysis System with Constant Data Polling

2020 ◽  
Vol 5 (11) ◽  
pp. 1-8
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Extraction ◽  
Community Services ◽  
Complex Data ◽  
Accurate Analysis ◽  
Early Disease Detection ◽  
Huge Data ◽  
Scope Of Application ◽  
Analysis System

The applications of Big data and machine learning in the fields of healthcare, bioinformatics and information sciences are the most important things that a researcher takes into consideration when doing predictive analysis. The Data production at this stage has never been higher and it is increasing at an alarming rate. Hence, it is difficult to store, process and visualise this huge data using customary technologies. However, abstract design for a specific massive information application has been restricted. With advancement of big data in the field of biomedical and healthcare domain, accurate analysis of medical data can be proved beneficial for early disease detection, patient care and community services. Machine learning is being used in a wide scope of application domains to discover patterns in huge datasets. Moreover, the results from machine learning drive critical decisions in applications relating healthcare and biomedicine. The transformation of data to actionable insights from complex data remains a key challenge. In this paper we have introduced a new method of polling of data before analysis is conducted on it. This method will be valuable for dealing with the issue of incomplete data and will progressively prompt suitable and more precise data extraction.

Personalized Disease Phenotypes from Massive OMICs Data

2015 ◽  
pp. 359-378 ◽  
Author(s):  
Hans Binder ◽  
Lydia Hopp ◽  
Kathrin Lembcke ◽  
Henry Wirth
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Worked Examples ◽  
Molecular Medicine ◽  
Diagnosis And Treatment ◽  
Complex Data ◽  
Omics Data ◽  
Self Organizing Maps ◽  
Disease Phenotypes ◽  
Disease Entities

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such “big data.” Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

Facies classification using machine learning

2016 ◽  
Vol 35 (10) ◽  
pp. 906-909 ◽  
Author(s):  
Brendon Hall
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Data Sets ◽  
Complex Data ◽  
Facies Classification ◽  
Large Complex ◽  
Complex Data Sets ◽  
Software Platforms ◽  
Tools And Techniques

There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscientists, meanwhile, have been doing science with voluminous data for years, without needing to brag about how big it is. But now that large, complex data sets are widely available, there has been a proliferation of tools and techniques for analyzing them. Many free and open-source packages now exist that provide powerful additions to the geoscientist's toolbox, much of which used to be only available in proprietary (and expensive) software platforms.

scholarly journals PNNCP- Parallel Nearest Neighbor Classification and Prediction for Big Data Application Based on Apache Spark and Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2358-2365
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Streams ◽  
Nearest Neighbor ◽  
Apache Spark ◽  
Course Of Action ◽  
Huge Data ◽  
Data Application ◽  
Big Data Application ◽  
Neighbor Classification

Right by and by the Colossal Information applications, for case, social orchestrating, helpful human administrations, agribusiness, keeping cash, stock show, direction, Facebook and so forward are making the data with especially tall speed. Volume and Speed of the Immense data plays a fundamental bit interior the execution of Colossal data applications. Execution of the Colossal data application can be affected by distinctive parameters. Quickly watch, capacity and precision are the a significant parcel of the triumphant parameters which impact the by and gigantic execution of any Huge data applications. Due the energize and underhanded affiliation of the qualities of 7Vs of Colossal data, each Colossal Information affiliations expect the tall execution.Tall execution is the foremost obvious test within the display advancing condition. In this paper we propose the parallel course of action way to bargain with speedup the explore for closest neighbor center. k-NN classifier is the preeminent basic and comprehensively utilized method for gathering. In this paper we apply a parallelism thought to k-NN for looking the another closest neighbor. This neighbor center will be utilized for putting lost and execution of the remarkable data streams. This classifier unequivocally overhaul and coordinate of the out of date data streams. We are utilizing the Apache Begin and scattered estimation space affiliation for snappier evaluation.

scholarly journals Big Data and Shipping-managing vessel performance

2018 ◽  
Vol 2 (2) ◽  
pp. 73
Author(s):  
Mandeep Virk ◽  
Vaishali Chauhan
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Fault Tolerance ◽  
High Rate ◽  
Shipping Industry ◽  
Large Sets ◽  
Huge Data ◽  
Reliable Performance ◽  
Increasing Demand

Shipping business is staggering the trade by a substantial number which portrays the usage of leading technologies to deliver formative and reliable performance to deal with the increasing demand. Technologies like AIS, machine learning, and IoT are making a shift in shipping industry by introducing robots and more sensor equipped devices. The hitch big data originates as a technology which is proficient for assembling and transforming the colossal and divergent figures of data providing organizations with meaningful insights for better decision-making. The size of data is increasing at a higher rate because of the procreation of peripatitic gadgets and sensors attached. Big data is accustomed to delineate technologies and techniques which are used to store, manage, distribute and analyze huge data sheets with a high rate of data occurrence. This gigantic data is allowing to terminate the business by developing meaningful and valuable insights by processing the data. Hadoop is the fundamental basic for composing big data and furnishes with convenient judgments through analysis. It enables the processing of large sets of data by providing a higher degree of fault-tolerance. Parallelism is adapted to process big size of data in the efficient and inexpensive way. Contending massive bulk of data is a determined and vigorous assignment that needs an enormous crunching armature to guaranty affluent data processing and analysis. 

scholarly journals Big Data and Machine Learning Integration: The Benefits and Research Issues in the Huge Data Processing

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2427-2429
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Distributed Storage ◽  
Current Work ◽  
Map Reduce ◽  
Individual Member ◽  
Research Issues ◽  
Huge Data ◽  
Hadoop Distributed File System ◽  
Processing Techniques

The generation of the data from individual member to MNC incurring more burden on the existing architectures. The current requirements of processing and storing huge data may not be suitable to the existing storage and processing techniques. The fundamental issue is kind of the data populated every second in the social media even reaching to peta bytes of the storage the processing of this huge data is another problem. Here the concept of big data comes into the picture,Hadoop is a frame work which is helpful to store huge amounts of the data and to process the data in parallel and distributed mode. The framework is the combination of Hadoop Distributed File System(HDFS) and Map Reduce(MR). HDFS is a distributed storage which allows huge storage capacity solves the issue of abnormal data population, whereas the processing of the data is taken by the Map Reduce which provides a versatile model of processing the huge amounts of the data. The other dimension of the current work is to analyze the huge amounts of the data which is beyond the scope of Hadoop based tools. Machine Learning (ML) is a class of algorithms provides various techniques to analyze the huge data in a better possible way. ML provides classification techniques, clustering mechanisms and Recommender systems to name a few. The importance of the current work is to integrate the Hadoop and R which in turn the combination of Big data and ML. The work provides the key benefits of such integration and future scope of the integration along with possible research constraints in the reality. We believe the work gives a platform to researchers so as to extract the future scope of the integration and difficulties faced in the process.

scholarly journals Machine Learning Based On Big Data Extraction of Massive Educational Knowledge

2017 ◽  
Vol 12 (11) ◽  
pp. 151 ◽  
Author(s):  
Abdelladim Hadioui ◽  
Nour-eddine El Faddouli ◽  
Yassine Benjelloun Touimi ◽  
Samir Bennani
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Analytics ◽  
Data Extraction ◽  
Learning System ◽  
Massive Data ◽  
Data Usage ◽  
E Learning ◽  
Uniform Model ◽  
Educational Field

A learning environment generates massive knowledge by means of the services provided in MOOCs. Such knowledge is produced via learning actor interactions. This result is a motivation for researchers to put forward solutions for big data usage, depending on learning analytics techniques as well as the big data techniques relating to the educational field. In this context, the present article unfolds a uniform model to facilitate the exploitation of the experiences produced by the interactions of the pedagogical actors. The aim of proposing the said model is to make a unified analysis of the massive data generated by learning actors. This model suggests making an initial pre-processing of the massive data produced in an e-learning system, and it’s subsequently intends to produce machine learning, defined by rules of measures of actors knowledge relevance. All the processing stages of this model will be introduced in an algorithm that results in the production of learning actor knowledge tree.

Review of machine learning applications to power systems studies

2021 ◽  
Vol 1 (1) ◽  
pp. 021-031
Author(s):  
Omorogiuwa Eseosa ◽  
Ashiathah Ikposhi
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Electricity Consumption ◽  
Complex Data ◽  
Accurate Analysis ◽  
The Future ◽  
Electric Power Networks ◽  
Machine Learning Applications ◽  
Past Experiences ◽  
Systems Studies

The complexity of electric power networks from generation, transmission and distribution stations in modern times has resulted to generation of big and more complex data that requires more technical and mathematical analysis because it deals with monitoring, supervisory control and data acquisition all in real time. This has necessitated the need for more accurate analysis and predictions in power systems studies especially under transient, uncertainty or emergency conditions without interference of humans. This is necessary so as to minimize errors with the aim targeted towards improving the overall performance and the need to use more technical but very intelligent predictive tools has become very relevant. Machine learning (ML) is a powerful tool which can be utilized to make accurate predictions about the future nature of data based on past experiences. ML algorithms operate by building a model (mathematical or pictorial) from input examples to make data driven predictions or decisions for the future. ML can be used in conjunction with big data to build effective predictive systems or to solve complex data analytic problems. Electricity generation forecasting systems that could predict the amount of power required at a rate close to the electricity consumption have been proposed in several works. This study seeks to review machine learning applications to power system studies. This paper reviewed applications of ML tools in power systems studies.

scholarly journals Machine Learning Based Method for Prediction of Heart Disease in Big Data Environment

2020 ◽  
Vol 9 (4) ◽  
pp. 1917-1921
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Historical Data ◽  
Heart Diseases ◽  
Medical Professional ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Huge Data ◽  
Learning Techniques ◽  
Non Linear

Prediction of diseases is one of the challenging tasks in healthcare domain. Conventionally the heart diseases were diagnosed by experienced medical professional and cardiologist with the help of medical and clinical tests. With conventional method even experienced medical professional struggled to predict the disease with sufficient accuracy. In addition, manually analysing and extracting useful knowledge from the archived disease data becomes time consuming as well as infeasible. The advent of machine learning techniques enables the prediction of various diseases in healthcare domain. Machine learning algorithms are trained to learn from the existing historical data and prediction models are being created to predict the unknown raw data. For the past two decades, machine learning techniques are extensively employed for disease prediction. Despite the capability of machine algorithm on learning from huge historical data which is stored in data mart and data warehouses using traditional database technologies such as Oracle OnLine Analytical Processing (OLAP). The conventional database technologies suffer from the limitation that they cannot handle huge data or unstructured data or data that comes with speed. In this context, big data tools and technologies plays a major role in storing and facilitating the processing of huge data. In this paper, an approach is proposed for prediction of heart diseases using Support Vector Algorithm in Spark environment. Support Vector Machine algorithm is basically a binary classifier which classifies both linear and non-linear input data. It transforms the non-linear data into hyper plan with the help of different kernel functions. Spark is a distributed big data processing platform which has a unique feature of keeping and processing a huge data in memory. The proposed approach is tested with a benchmark dataset from UCI repository and results are discussed.

scholarly journals Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

2020 ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Huge Data ◽  
Learning Techniques

Abstract Outpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in Spark platform. The aim of this paper is exploring factors that affect no-sow rate then can be used to formulate predictions using big data machine learning techniques.

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